Intelligent Adjustment of Dialysis Machine Operations

ABSTRACT

A remote service is implemented to automatically aggregate data across hemodialysis patients and hemodialysis patients and determine updated treatment options for patients to increase well-being and optimize performance of the hemodialysis machines. Patients or caregivers operating a hemodialysis machine or a local or remote user computing device associated with the hemodialysis machine can provide feedback regarding the patient&#39;s well-being to the remote service. The feedback can be provided at any of one or more times pre-treatment, during treatment, or post-treatment. Furthermore, the hemodialysis machine can be configured with one or more sensors that transmit data pertaining to device state of the hemodialysis machine, such as information about blood, dialysate used, saline solution, pump pressure, air trap and air detector, hemodialysis machine information (e.g., make and model), etc.

CROSS-REFERENCES TO RELATED APPLICATIONS

This Non-Provisional Patent Application is a Continuation of U.S.Non-Provisional Application Ser. No. 16/535,036, filed Aug. 7, 2019,entitled “Intelligent Adjustment of Dialysis Machine Operations,” theentire contents of which is hereby incorporated herein by reference.

BACKGROUND

Hemodialysis machines are utilized to filter a patient's blood due torenal failure, in which numerous components and functions are utilizedby the dialysis machine during filtration. The components andfunctionality for a given hemodialysis machine can vary by patient, suchas a rate at which blood is pumped, composition of dialysate, amongother variances. In some scenarios, patients can experience discomfortduring or after treatment, such as overall discomfort, muscle cramps,dizziness, etc. As portable dialysis treatments become increasinglypopular, adjusting treatment can provide increased patient comfort,experience, and well-being.

SUMMARY

A remote service is configured to automatically aggregate data acrosshemodialysis patients and machines, recognize patterns, and determineupdated treatment options for patients to increase well-being andoptimize performance of the hemodialysis machine. Patients, caregivers,or otherwise users operating a hemodialysis machine or a local or remotecomputing device associated with the hemodialysis machine can providefeedback regarding the patient's well-being to the remote service. Useof the term “user” herein can refer to the patient or a caregiveroperating the hemodialysis machine or computing device for the patient.The feedback can be provided at any of one or more times includingpre-treatment, during treatment, or post-treatment. Furthermore, thehemodialysis machine can be configured with one or more sensors thattransmit data pertaining to device state of the hemodialysis machine,such as information about blood, dialysate used, saline solution, pumppressure, air trap and detector, hemodialysis machine information (e.g.,make and model), etc.

Upon receiving the user's feedback and crowd-sourced feedback from otherhemodialysis users, the remote service can identify patterns in thedata. The remote service can utilize an artificial intelligence engine(AI engine) to identify patterns and provide useful predictions forpatients' treatments and hemodialysis machine configurations. The AIengine can ingest the data (e.g., user feedback, device stateinformation, etc.), clean, prepare and manipulate the data, train amodel, test the model, and then deploy a generated predictive model. Insome exemplary scenarios, the remote service can remotely control andautomatically adjust functions or operations of the hemodialysis machine(e.g., dialysate composition, temperature of dialysate, etc.), or cantransmit a notification to the patient or caregiver regarding arecommended change in treatment or change in the hemodialysis machine'soperation. In one exemplary scenario, the remote service canautomatically adjust a concentration of ingredients in dialysate (e.g.,the bicarbonate, acid, or water) when like patients, using a similardialysate composition, are experiencing common symptoms. Thenotification can be transmitted and displayed on a display of thehemodialysis machine itself, or on a user computing device remote fromthe machine.

In some implementations, the hemodialysis machine can utilize processorsor System on a Chips (SoCs) to control operations of the hemodialysismachine. For example, an SoC may be specifically configured to controltemperature or composition of dialysate. The remote service can transmita command to the SoC or processor associated with the hemodialysismachine to adjust operations in situations in which the remote serviceautomatically controls machine operations.

Utilization of crowd-sourced feedback and the remote service's AI engineenables both experienced and inexperienced users of hemodialysismachines to benefit from pattern recognition techniques and an optimizedmachine. Furthermore, in some situations, the remote service canrecognize faults with the hemodialysis machine and thereby remotelycorrect the issue or transmit a notification to the user for correction.Optimizing the hemodialysis machine's operations can prolong themachine's life, optimize its settings, reduce the possibility ofcomponent or machine failure, and overall increase performance of themachine, all while increasing user comfort, care, and well-being.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter. Furthermore, the claimed subject matter is not limited toimplementations that solve any or all disadvantages noted in any part ofthis disclosure. It will be appreciated that the above-described subjectmatter may be implemented as a computer-controlled apparatus, a computerprocess, a computing system, or as an article of manufacture such as oneor more computer-readable storage media. These and various otherfeatures will be apparent from a reading of the following DetailedDescription and a review of the associated drawings.

DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an illustrative environment in which crowd-sourced datafrom hemodialysis machines and computing devices is utilized by theremote service to responsively transmit settings and/or notifications;

FIG. 2 shows a layered architecture of an illustrative hemodialysismachine;

FIG. 3 shows a taxonomy of illustrative objects monitored by the remoteservice;

FIG. 4 shows a taxonomy of illustrative sensors implemented onhemodialysis machines;

FIG. 5 shows an illustrative environment in which a user enters feedbackinto a user interface associated with the hemodialysis machine orcomputing device for transmission to a remote service;

FIG. 6 shows an illustrative diagram in which an artificial intelligence(AI) engine associated with the remote service creates a predictivemodel using received data;

FIG. 7 shows an illustrative diagram in which the remote serviceperforms pattern recognition on received user feedback and/or monitoreddata;

FIG. 8 shows a taxonomy of illustrative pattern recognition featuresutilized by the remote service;

FIG. 9 shows a taxonomy of illustrative setting modifications andnotifications transmitted from the remote service to hemodialysismachines;

FIG. 10 shows illustrative notifications presented on a display of auser's hemodialysis machine;

FIG. 11 shows an illustrative environment in which the remote servicesends adjustments to a processor or system on a chip (SoC) associatedwith the hemodialysis machine;

FIGS. 12-14 are flowcharts of illustrative methods performed by ahemodialysis machine, remote service, or computing device;

FIG. 15 shows a diagram of an illustrative hemodialysis machine toimplement the present automated adjustment of dialysis machineoperations;

FIG. 16 is a simplified block diagram of an illustrative architecture ofa computing device that may be used at least in part to implement thepresent automated adjustment of dialysis machine operations; and

FIG. 17 is a simplified block diagram of an illustrative remotecomputing device, remote service, or computer system that may be used inpart to implement the present automated adjustment of dialysis machineoperations.

Like reference numerals indicate like elements in the drawings. Elementsare not drawn to scale unless otherwise indicated.

DETAILED DESCRIPTION

FIG. 1 shows an illustrative environment in which crowd-sourced datafrom hemodialysis machines 105 and/or computing devices 160 are receivedand utilized by a remote service 110 to responsively transmit treatmentsettings 140 and notifications 145 to the hemodialysis machines orassociated computing devices. Such transmissions can occur over thenetwork 115, which can include any one or more of a local area network,wide area network, Internet, or World Wide Web.

On a per-user 135 basis, the hemodialysis machines 105 can monitor datasurrounding treatment (e.g., utilized components and settings of thehemodialysis machine, patient information, etc.) for a patient 155 andtransmit such data to the remote service 110. User feedback at thehemodialysis machines or associated computing devices can likewise betransmitted to the remote service. Collectively, the crowd-sourced data120 enables the remote service to analyze the data across many patientsand treatment sessions. As discussed in greater detail below, the remoteservice can analyze the received data, develop new treatment plans,notifications, or operation adjustments based on each patient's uniquesituation, and transmit adjustments or notifications to the patient'srespective hemodialysis machine. The signals can be transmittedpre-treatment, during treatment, or post treatment, as representativelyshown by numeral 150.

FIG. 2 shows a simplified layered architecture 200 of the hemodialysismachine 105. The machine can include a hardware layer 220, operatingsystem (OS) layer 215, and application layer 210. The hardware layer 215provides an abstraction of the various hardware used by the host device105 (e.g., input and output devices, networking and radio hardware,etc.) to the layers above it. In this illustrative example, the hardwarelayer supports processor(s) 225, memory 230, input/output devices (e.g.,mouse, keyboard, display) 240, operational dialysis components 245, andsensors 250 for sensing operations of the hemodialysis machine (e.g.,pump speed, blood or saline composition, etc.). A description of theoperational dialysis components is discussed in greater detail withrespect to FIG. 15. Although not shown, the hemodialysis machine canalso include a network interface card (NIC) which enables a wired orwireless connection to the Internet, such as through a router. This canenable the hemodialysis machine to communicate with the remote service110 (e.g., transmit data and feedback, receive settings andnotifications, etc.). In some implementations, the hemodialysis machinecan support short-range communications over Bluetooth™ or NFC (NearField Communication), such as to a user's personal computing device(e.g., smartphone, tablet computer, personal computer (PC), laptop,etc.).

The application layer 210 in this illustrative example supports variousapplications 270, a device state application 275 that transmitsinformation about the hemodialysis machine to the remote service, and auser feedback application 265 which enables input of user feedback intothe hemodialysis machine and transmission to the remote service 110. Insome implementations, the user's computing device 160 can be configuredto receive user feedback and communicate with the remote service 110.Although the distinct applications are depicted in FIG. 2, theapplications may alternatively operate within a same application, as aplugin to other applications or the OS, or interoperate with remotelyexecuting code, such as with the remote service.

Although only certain applications are depicted in FIG. 2, any number ofapplications can be utilized by the hemodialysis machine. Theapplications are often implemented using locally executing code. In somecases, however, these applications can rely on services and/or remotecode execution provided by remote servers or other computing platformssuch as those supported by a service provider or other cloud-basedresources (not shown).

The OS layer 215 supports, among other operations, managing system 255and operating applications/programs 260. The OS layer may interoperatewith the application and hardware layers in order to perform variousfunctions and features.

FIG. 3 shows a taxonomy of objects which the hemodialysis machine maymonitor, such as using the device state application 275 (FIG. 2), asrepresentatively shown by numeral 305. Exemplary and non-exhaustiveobjects can include blood 310, dialysate 315, saline solution 320, pumppressure 325, air trap and detector 330, user input information (e.g.,type of dialysate used) 335, local device information (e.g., make andmodel, type of pressure pump, etc.) 340, and other features orcomponents associated with the hemodialysis machine 105. The objects canbe monitored according to one or more various metrics 350, such as aconcentration (e.g., concentration of dialysate or blood), ratio,composition (e.g., composition of the dialysate), volume, rate (e.g.,rate at which blood is pumped from the patient), among other metrics.

FIG. 4 shows a taxonomy of sensors which may be implemented within thehemodialysis machine 105, as representatively shown by numeral 405.Exemplary and non-exhaustive sensors include a pressure sensor (e.g.,for pump pressure) 410, temperature sensor 415 (e.g., for dialysate),blood pressure sensor 420, urea sensor 425, volume sensor 430,concentration sensor 435, weight sensor (e.g., a scale) 440, and othersensors 445. The sensors may be utilized to gather information and dataabout the hemodialysis machine, components within the machine, and itsoperations and transmit the gathered data to the remote service.

FIG. 5 shows an illustrative user interface 515 associated with the userfeedback application 265 on a display of either the computing device 160or hemodialysis machine 105. The user interface depicts one possiblefeedback mechanism by which patients or medical providers can providefeedback to the remote service for analysis. In this illustrativeexample, questions 520 are posed, to which the patient can respond usinga 0-10 rating, as representatively shown by numeral 525. That is, thepatient can rate a degree of comfort or discomfort for each posedquestion. While a particular feedback style is depicted in FIG. 5, otherforms of feedback are also possible, such as general user comments,radio buttons that indicate yes/no or the presence/absence of acondition, checkboxes, text boxes for input, etc. In some scenarios, thefeedback may be in format that is consumable for processing by anartificial intelligence engine, such that the data is properly dividedor separated (e.g., by commas). The data may be in the appropriateformat upon users entering the data or may be reconfigured upon receiptat the remote service. The user can provide the feedback pre-treatment510, post-treatment 505, or both.

FIG. 6 shows an illustrative diagram in which the remote service 110employs an artificial intelligence (AI) engine 605 to recognize patternsamong hemodialysis patients. The AI engine utilizes an algorithm todevelop a predictive model based on the crowd-sourced data. In step 610,the AI engine ingests the data 610 (e.g., the user feedback and devicestate information). In step 615, the AI engine may clean, prepare, andmanipulate the data. For example, the data may be randomized, to reducethe possibility of an order affecting the machine learning process, andseparated, between a training set for training the model and a testingset for testing the trained model. Other forms of data manipulation maybe performed as well, such as normalization, error correction, and thelike.

In steps 620 and 625, the AI engine trains and tests the model,respectively. The model training may be used to incrementally improvethe model's ability to make accurate predictions. The model training mayuse the features contained in the data to form a matrix with weights andbiases against the data. Random values within the data may be utilizedto attempt prediction of the output based on those values. This processmay repeat until a more accurate model is developed which can predictcorrect outputs. The model may subsequently be evaluated to determine ifit meets some accuracy threshold (e.g., 70% or 80% accuracy), and thenthe predictive model will be deployed to make predictions at step 630.

While the AI engine is one method by which the remote service candevelop pattern recognitions among the crowd-sourced data, other methodsof pattern recognition are also possible. Such as using hard codedmethods in an algorithm which creates correlations between a patient'sexperiences and conditions with the hemodialysis treatment information(FIG. 3).

FIG. 7 shows an illustrative environment in which the remote service110, upon receiving the feedback, recognizes patterns among thepatients, as representatively shown by numeral 705. For example, thegroup of patients 650 may all experience overall well-being 710, inwhich the remote service recognizes that the patients spend four hoursper hemodialysis treatment session 725, utilize similar types ofdialysate 730, and change parts regularly 735. The remote service mayhave identified these commonalities among patients who are experiencingwell-being. This data can be used when assessing patients who are havingnegative experiences with their treatment, in which the remote servicecan make an adjustment or transmit a notification to the user to make achange that comports with the setup of the group 750.

The remote service 110 may recognize that the group of patients 755experiencing muscle cramps 715 may each be associated with an excessamount of urea 740, detected by a sensor in the hemodialysis machine.The remote service may recognize that the group of patients 760 may beexperiencing dizziness during treatment 720, which is typicallyassociated with high pressure pump 745, also detected by a sensor withinthe machine. Other examples of pattern recognition using thecrowd-sourced data is also possible.

FIG. 8 shows a taxonomy of pattern recognition features (AI or non-AIdriven) that the remote service can perform, as representatively shownby numeral 805. The remote service can identify corresponding feedbackwith device state information 810; identify corresponding symptoms oruser experiences with device state information 815; identifycorresponding symptoms or user experiences based on user characteristics(e.g., age, blood, pressure), device state information, and/or feedback820; responsive to identifying a pattern, transmit a command to altersettings with a dialysis machine to improve user experiences and usersymptoms 825; transmit notifications to a dialysis machine or user'sdevice for suggested modifications to settings and/or replace or repaircomponents or fluids 830; transmit crowd-sourced statistics with commandor notification (e.g., percentage of patients experiencing less or nosymptoms with a particular dialysate composition) 835; verify commandsand information in notifications comport with specific patient's medicalplan guidelines 840; and other features 845.

FIG. 9 shows a taxonomy of setting modifications and notificationcontent that the remote service may transmit to a user's hemodialysismachine or computing device based on the remote service's analysis ofreceived data, as representatively shown by numeral 905. Transmittedsetting and notification content can include adjust concentration ofingredients in dialysate 910, which can include decreasing sodium 915 orincreasing sodium 920, adjust composition of dialysate 925 (e.g.,adjusting one or more of the bicarbonate, acid, or water as typicallyutilized for dialysate), adjust temperature of dialysate (e.g., cooleror warmer) 930, adjust pump speed (e.g., slower or faster) 935, adjustduration of dialysate treatment 940, recommend duration and frequency oftreatment 945, recommend to switch type (e.g., brand or composition) ofdialysate 955, or recommend to replace or fix a component (e.g., a hoseor pump) 955. Sensors inside the hemodialysis machine may detectcomponent performance in which the remote service, using the crowdsourced data, can recognize that the machine may have a defectivecomponent (e.g., hose, pump, etc.) and replacing the component has beenassociated with increased performance.

FIG. 10 shows an environment of illustrative notifications 1005 and 1010displayed on a user interface 515 of a hemodialysis machine 105 orcomputing device 160. Other types of user interfaces can includeauditory sounds through speakers, haptic feedback, etc. Notification1005 explains to the user that he may experience less muscle cramps ifhe reduces the sodium concentration and, before transmitting thenotification, verifies that the recommendation follows the patient'sspecific medical guidelines and treatment plan. Notifications 1010 eachprovide unique recommendations to the user to improve their overallwell-being and/or improve the machine's performance. While FIG. 10illustrates the remote service transmitting notifications to the user'sdevice and providing the user the option of making a treatmentadjustment, in some scenarios, the remote service can automaticallyadjust a component or operation of the hemodialysis machine. Forexample, the remote service can reduce or increase the pump speed if itrecognizes that the user's health can be improved by doing so. In somescenarios, the remote service may transmit a notification to the patientthat alerts him of the automated adjustment after or upon doing so.

FIG. 11 shows an environment in which the remote service 110communicates with a processor 1115 or system on a chip (SoC) 1120associated with and implemented by the hemodialysis machine. Theprocessor or SoC may be associated with one or more distinct machinecomponents 1125, 1130 in order to enable the remote service to transmitan adjustment 1105, 1110 that the hemodialysis machine can execute. Forexample, the SoC may operate the blood pump and thereby can regulate itsflow speed. In some implementations, the SoC may employ one or moresensors to gather data at the hemodialysis machine, and can also beconfigured to adjust the settings or operations of the same componentresponsive to, for example, an instructional adjustment from the remoteservice. As another example, an SoC can control a pump that controls themake of the dialysate, such as by adjusting the bicarbonate, acid, orwater.

FIGS. 12-14 are flowcharts of exemplary methods 1200, 1300, and 1400that may be implemented by one or more of a hemodialysis machine,computing device associated with the machine, or a remote service.Unless specifically stated, the methods or steps shown in the flowchartsand described in the accompanying text are not constrained to aparticular order or sequence. In addition, some of the methods or stepsthereof can occur or be performed concurrently and not all the methodsor steps have to be performed in a given implementation depending on therequirements of such implementation and some methods or steps may beoptionally utilized.

In step 1205, in FIG. 12, a hemodialysis machine generates, using one ormore sensors, data for monitored device state characteristics, includingfor various liquids that pass through the dialysis machine, duringtreatment. In step 1210, the hemodialysis machine transmits thegenerated data to a remote servicer. In step 1215, the hemodialysismachine receives, from the remote service, one or more automatedadjustments to operations at the hemodialysis machine based on thetransmitted generated data.

FIG. 13 shows a flowchart of a method 1300 which may be performed by aremote service. In step 1305, the remote service user feedback regardingtreatment using a hemodialysis machine. In step 1310, the remote servicereceives information for dialysis machines for respective users. In step1315, the remote service associates information for the dialysis machinewith respective user feedback. In step 1320, the remote serviceidentifies patterns within the associated information, including aconcentration or composition of used dialysate and associated userwell-being. In step 1325, the remote service, using the identifiedpatterns, transmits updated settings or notifications to a dialysismachine to facilitate greater user experiences and well-being.

FIG. 14 shows a flowchart of a method 1400 which may be performed by ahemodialysis machine or associated computing device. In step 1405, thehemodialysis machine sets criteria for a patient, in which the criteriaare limits or configurations for operating the machine for the patient.In step 1410, the hemodialysis machine receives data pertaining tooperations of the hemodialysis machine or characteristics of thepatient. In step 1415, the hemodialysis machine transmits the receiveddata to a remote computing device. In step 1420, the hemodialysismachine receives, from the remote computing device, one or moreadjustments to an operation of the hemodialysis machine or anotification about the machine. In step 1425, the hemodialysis machineverifies that the received one or more adjustments or notifications arein-line with the set criteria for the patient.

FIG. 15 is an exemplary hemodialysis machine 1500 which may beimplemented and utilized for the purposes described herein. The diagramshows the various components and operations of the hemodialysis machine,but other components not shown are also possible. The hemodialysismachine, using the blood pump 1520, removes blood from the patient 1510,as representatively shown by numeral 1505. An arterial pressure monitor1515 may be implemented to regulate the amount of pressure and ensurethat excessive negative pressure is not generated. A heparin pump 1525injects a regulated amount of heparin into the blood within the tubewhile the patient is undergoing treatment. Heparin may be utilized toprevent blood clotting during treatment.

Before entering the dialyzer 1565, the blood flows through an inflowpressure monitor 1530 to regulate the inflow of blood. Saline solution1545 is can be utilized to flush the system and cleanse the blood whichis to be flow back into the patient through the hemodialysis system. Theblood enters the dialyzer which is responsible for removing wastes likeurea and adding sodium bicarbonate to correct blood acidity. The processby which the dialyzer purifies the blood for bodily use is diffusion, inwhich the artificial filter of the dialyzer employs fibers, dialysate,and a semi-permeable membrane through which the blood flows. Useddialysate 1535 flows to a waste container and fresh dialysate 1540 ispulled into the dialyzer 1565.

Once the blood advances through the dialyzer 1665, a venous pressuremonitor 1550 may be utilized to measure the flow of cleansed bloodthrough the user's vein and into the body. An air trap and air detector1555 is utilized to make sure no air enters the user's venous needle andvein when moving the cleansed blood back into the user's body. The cleanblood 1660 then enters through the user's body through the final portionof the tube, through a needle, and into the user's vein.

FIG. 16 shows an illustrative architecture 1600 for a client computingdevice such as a laptop computer or personal computer for the presentautomated adjustment of dialysis machines. The architecture 1600illustrated in FIG. 16 includes one or more processors 1602 (e.g.,central processing unit, dedicated Artificial Intelligence chip,graphics processing unit, etc.), a system memory 1604, including RAM(random access memory) 1606 and ROM (read only memory) 1608, and asystem bus 1610 that operatively and functionally couples the componentsin the architecture 1600. A basic input/output system containing thebasic routines that help to transfer information between elements withinthe architecture 1600, such as during startup, is typically stored inthe ROM 1608. The architecture 1600 further includes a mass storagedevice 1612 for storing software code or other computer-executed codethat is utilized to implement applications, the file system, and theoperating system. The mass storage device 1612 is connected to theprocessor 1602 through a mass storage controller (not shown) connectedto the bus 1610. The mass storage device 1612 and its associatedcomputer-readable storage media provide non-volatile storage for thearchitecture 1600. Although the description of computer-readable storagemedia contained herein refers to a mass storage device, such as a harddisk or CD-ROM drive, it may be appreciated by those skilled in the artthat computer-readable storage media can be any available storage mediathat can be accessed by the architecture 1600.

By way of example, and not limitation, computer-readable storage mediamay include volatile and non-volatile, removable and non-removable mediaimplemented in any method or technology for storage of information suchas computer-readable instructions, data structures, program modules, orother data. For example, computer-readable media includes, but is notlimited to, RAM, ROM, EPROM (erasable programmable read only memory),EEPROM (electrically erasable programmable read only memory), Flashmemory or other solid state memory technology, CD-ROM, DVD, HD-DVD (HighDefinition DVD), Blu-ray, or other optical storage, magnetic cassette,magnetic tape, magnetic disk storage or other magnetic storage device,or any other medium which can be used to store the desired informationand which can be accessed by the architecture 1600.

According to various embodiments, the architecture 1600 may operate in anetworked environment using logical connections to remote computersthrough a network. The architecture 1600 may connect to the networkthrough a network interface unit 1616 connected to the bus 1610. It maybe appreciated that the network interface unit 1616 also may be utilizedto connect to other types of networks and remote computer systems. Thearchitecture 1600 also may include an input/output controller 1618 forreceiving and processing input from a number of other devices, includinga keyboard, mouse, touchpad, touchscreen, control devices such asbuttons and switches or electronic stylus (not shown in FIG. 16).Similarly, the input/output controller 1618 may provide output to adisplay screen, user interface, a printer, or other type of outputdevice (also not shown in FIG. 16).

It may be appreciated that the software components described herein may,when loaded into the processor 1602 and executed, transform theprocessor 1602 and the overall architecture 1600 from a general-purposecomputing system into a special-purpose computing system customized tofacilitate the functionality presented herein. The processor 1602 may beconstructed from any number of transistors or other discrete circuitelements, which may individually or collectively assume any number ofstates. More specifically, the processor 1602 may operate as afinite-state machine, in response to executable instructions containedwithin the software modules disclosed herein. These computer-executableinstructions may transform the processor 1602 by specifying how theprocessor 1602 transitions between states, thereby transforming thetransistors or other discrete hardware elements constituting theprocessor 1602.

Encoding the software modules presented herein also may transform thephysical structure of the computer-readable storage media presentedherein. The specific transformation of physical structure may depend onvarious factors in different implementations of this description.Examples of such factors may include, but are not limited to, thetechnology used to implement the computer-readable storage media,whether the computer-readable storage media is characterized as primaryor secondary storage, and the like. For example, if thecomputer-readable storage media is implemented as semiconductor-basedmemory, the software disclosed herein may be encoded on thecomputer-readable storage media by transforming the physical state ofthe semiconductor memory. For example, the software may transform thestate of transistors, capacitors, or other discrete circuit elementsconstituting the semiconductor memory. The software also may transformthe physical state of such components in order to store data thereupon.

As another example, the computer-readable storage media disclosed hereinmay be implemented using magnetic or optical technology. In suchimplementations, the software presented herein may transform thephysical state of magnetic or optical media, when the software isencoded therein. These transformations may include altering the magneticcharacteristics of particular locations within given magnetic media.These transformations also may include altering the physical features orcharacteristics of particular locations within given optical media tochange the optical characteristics of those locations. Othertransformations of physical media are possible without departing fromthe scope and spirit of the present description, with the foregoingexamples provided only to facilitate this discussion.

The architecture 1600 may further include one or more sensors 1614 or abattery or power supply 1620. The sensors may be coupled to thearchitecture to pick up data about an environment or a component,including temperature, pressure, etc. Exemplary sensors can include athermometer, accelerometer, smoke or gas sensor, pressure sensor(barometric or physical), light sensor, ultrasonic sensor, gyroscope,among others. The power supply may be adapted with an AC power cord or abattery, such as a rechargeable battery for portability.

In light of the above, it may be appreciated that many types of physicaltransformations take place in the architecture 1600 in order to storeand execute the software components presented herein. It also may beappreciated that the architecture 1600 may include other types ofcomputing devices, including wearable devices, handheld computers,embedded computer systems, smartphones, PDAs, and other types ofcomputing devices known to those skilled in the art. It is alsocontemplated that the architecture 1600 may not include all of thecomponents shown in FIG. 16, may include other components that are notexplicitly shown in FIG. 16, or may utilize an architecture completelydifferent from that shown in FIG. 16.

FIG. 17 is a simplified block diagram of an illustrative computer system1700 such as a PC or server with which the present automated adjustmentof dialysis machines may be implemented. Computer system 1700 includes aprocessor 1705, a system memory 1711, and a system bus 1714 that couplesvarious system components including the system memory 1711 to theprocessor 1705. The system bus 1714 may be any of several types of busstructures including a memory bus or memory controller, a peripheralbus, or a local bus using any of a variety of bus architectures. Thesystem memory 1711 includes read only memory (ROM) 1717 andrandom-access memory (RAM) 1721. A basic input/output system (BIOS)1725, containing the basic routines that help to transfer informationbetween elements within the computer system 1700, such as duringstartup, is stored in ROM 1717. The computer system 1700 may furtherinclude a hard disk drive 1728 for reading from and writing to aninternally disposed hard disk (not shown), a magnetic disk drive 1730for reading from or writing to a removable magnetic disk 1733 (e.g., afloppy disk), and an optical disk drive 1738 for reading from or writingto a removable optical disk 1743 such as a CD (compact disc), DVD(digital versatile disc), or other optical media. The hard disk drive1728, magnetic disk drive 1730, and optical disk drive 1738 areconnected to the system bus 1714 by a hard disk drive interface 1746, amagnetic disk drive interface 1749, and an optical drive interface 1752,respectively. The drives and their associated computer-readable storagemedia provide non-volatile storage of computer-readable instructions,data structures, program modules, and other data for the computer system1700. Although this illustrative example includes a hard disk, aremovable magnetic disk 1733, and a removable optical disk 1743, othertypes of computer-readable storage media which can store data that isaccessible by a computer such as magnetic cassettes, Flash memory cards,digital video disks, data cartridges, random access memories (RAMs),read only memories (ROMs), and the like may also be used in someapplications of the present automated adjustment of dialysis machines.In addition, as used herein, the term computer-readable storage mediaincludes one or more instances of a media type (e.g., one or moremagnetic disks, one or more CDs, etc.). For purposes of thisspecification and the claims, the phrase “computer-readable storagemedia” and variations thereof, are intended to cover non-transitoryembodiments, and do not include waves, signals, and/or other transitoryand/or intangible communication media.

A number of program modules may be stored on the hard disk, magneticdisk 1733, optical disk 1743, ROM 1717, or RAM 1721, including anoperating system 1755, one or more application programs 1757, otherprogram modules 1760, and program data 1763. A user may enter commandsand information into the computer system 1700 through input devices suchas a keyboard 1766 and pointing device 1768 such as a mouse. Other inputdevices (not shown) may include a microphone, joystick, game pad,satellite dish, scanner, trackball, touchpad, touchscreen,touch-sensitive device, voice-command module or device, user motion oruser gesture capture device, or the like. These and other input devicesare often connected to the processor 1705 through a serial portinterface 1771 that is coupled to the system bus 1714, but may beconnected by other interfaces, such as a parallel port, game port, oruniversal serial bus (USB). A monitor 1773 or other type of displaydevice is also connected to the system bus 1714 via an interface, suchas a video adapter 1775. In addition to the monitor 1773, personalcomputers typically include other peripheral output devices (not shown),such as speakers and printers. The illustrative example shown in FIG. 17also includes a host adapter 1778, a Small Computer System Interface(SCSI) bus 1783, and an external storage device 1776 connected to theSCSI bus 1783.

The computer system 1700 is operable in a networked environment usinglogical connections to one or more remote computers, such as a remotecomputer 1788. The remote computer 1788 may be selected as anotherpersonal computer, a server, a router, a network PC, a peer device, orother common network node, and typically includes many or all of theelements described above relative to the computer system 1700, althoughonly a single representative remote memory/storage device 1790 is shownin FIG. 17. The logical connections depicted in FIG. 17 include a localarea network (LAN) 1793 and a wide area network (WAN) 1795. Suchnetworking environments are often deployed, for example, in offices,enterprise-wide computer networks, intranets, and the Internet.

When used in a LAN networking environment, the computer system 1700 isconnected to the local area network 1793 through a network interface oradapter 1796. When used in a WAN networking environment, the computersystem 1700 typically includes a broadband modem 1798, network gateway,or other means for establishing communications over the wide areanetwork 1795, such as the Internet. The broadband modem 1798, which maybe internal or external, is connected to the system bus 1714 via aserial port interface 1771. In a networked environment, program modulesrelated to the computer system 1700, or portions thereof, may be storedin the remote memory storage device 1790. It is noted that the networkconnections shown in FIG. 17 are illustrative and other means ofestablishing a communications link between the computers may be useddepending on the specific requirements of an application of the presentautomated adjustment of dialysis machines.

Exemplary systems, devices, and methods are disclosed herein. In oneembodiment, a dialysis machine configured to automatically adjustoperations, comprising: a network interface; one or more sensors localto the dialysis machine; one or more processors operatively coupled tothe network interface and one or more sensors; and a hardware-basedmemory device having executable instructions which, when executed by theone or more processors, cause the dialysis machine to: generate, usingthe one or more sensors, data for monitored device statecharacteristics, including for various liquids that pass through thedialysis machine during a treatment session with a user; transmit thegenerated data to a remote service; and receive from the remote serviceone or more automated adjustments to operations at the hemodialysismachine based on the transmitted generated data, in which the receivedone or more adjustments include adjusting a concentration or compositionof dialysate or saline solution utilized during the hemodialysistreatment to calibrate an administered dosage.

In another example, the adjusted operations occurs after multipledifferent hemodialysis treatments. As another example, the remoteservice or hemodialysis machine verifies that the automated adjustmentcomports with treatment criteria for the patient. As another example,the remote service transmits the automated adjustment after identifyingpatterns using artificial intelligence processing of received data, andin which the transmitted automated adjustment is assessed based on theartificial intelligence processing of the data. As another example, theautomated adjustment is unique to the user and is determined using knownphysiological information about the user in combination with thegenerated sensor data. As another example, the automated adjustment isfurther determined using crowd-source information derived from dialysismachines during patient uses. As another example, the dialysis machineor a computing device associated with the user of the dialysis machineis configured to receive user feedback for their treatment, and the userfeedback is transmitted to the remote service in determining theadjusted operations for the hemodialysis machine.

Another exemplary embodiment includes a method performed by a remoteservice in communication with a dialysis machine, comprising: receivinguser feedback regarding treatment using a hemodialysis machine;receiving information for dialysis machines for respective users;associating information for the dialysis machines with respective userfeedback; identifying patterns within the associated information,including a concentration or composition of used dialysate andassociated user well-being; and using the identified patterns,transmitting updated settings or notifications to a dialysis machine tofacilitate greater user experiences and well-being.

As another example, the user feedback is received pre-treatment,post-treatment, or during treatment. As another example, the updatedsettings or notifications include any one or more of adjusting aconcentration or composition of a dialysate; adjusting pump pressure toincrease or decrease a rate at which blood is pumped from the user;adjust rate at which filtration is performed; a notification on a userinterface to change water filter; or a notification on the userinterface to change a dialysate membrane. As another example, the userinterface includes any one or more of a visualization on a displayscreen, an auditory sound using a speaker, haptic feedback, or agesture. As another example, the user feedback includes user-experiencedsymptoms pre-treatment and during the treatment, and general well-beingof the user. As another example, types of user feedback can include userresponses to a questionnaire including multiple choice and fill-in theblank, free-form user information, and a rating system. As anotherexample, information and feedback are received from a plurality ofusers, pattern identification includes identifying operational settingsfor the hemodialysis machine and its operational components for usersexperiencing positive or general well-being based on the feedback, andfurther comprising using the pattern identifications, transmitting theupdated settings or notifications to the dialysis machine using theidentified operational settings for the user's experiencing positive orgeneral well-being. As another example, information and feedback arereceived from a plurality of users, and pattern determination includesidentifying operational settings for the hemodialysis machine and itsoperational components for users experiencing negative or poorwell-being, and further comprising: using the pattern determinations,transmitting updated settings and notifications to the hemodialysismachine to preemptively address operational settings which correspond tonegative experiences or poor well-being for users.

In another embodiment, one or more hardware-based non-transitorycomputer-readable memory devices stored within a hemodialysis machinethat is configured to filter blood from a patient, the memory devicesincluding instructions which, when executed by one or more processors,cause the hemodialysis machine to: set criteria for a patient, in whichthe criteria are limits or configurations for operating the machine forthe patient; receive data pertaining to operations of the hemodialysismachine or characteristics of the patient, in which the received data isreceived from the patient or generated by the hemodialysis machine;transmit the received data to a remote computing device; receive, fromthe remote computing device, one or more adjustments to an operation ofthe hemodialysis machine or a notification about the hemodialysismachine or the patient, wherein the remote computing device determinesthe one or more adjustments using an artificial intelligence (AI) enginebased on crowd-sourced data obtained from at least other hemodialysispatients; and verifying that the received one or more adjustments ornotification are in-line with the set criteria for the patient.

In another example, the AI engine of the remote computing device trainsa model of data and deploys a predictive model to determine the one ormore automated adjustments for the hemodialysis machine. As anotherexample, the generated or received data include information forprocessed blood or dialysate or pump operation. As another example, areceived adjustment includes adjusting a concentration or composition ofdialysate or sodium solution utilized during a dialysis treatment tocalibrate an administered dosage, or adjusting pump pressure. As anotherexample, the one or more adjustments or notification is derived fromcrowd-sourced information for dialysis treatments associated withrespective users that are relatable and applicable to the patient.

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described above.Rather, the specific features and acts described above are disclosed asexample forms of implementing the claims.

1-20. (canceled)
 21. One or more hardware-based non-transitorycomputer-readable memory devices stored within a medical deviceassociated with a patient, the medical device adapted to providetreatment to the patient, wherein the memory devices includeinstructions which, when executed by one or more processors, cause themedical device to: collect data pertaining to operations of the medicaldevice or characteristics of the patient, in which the data collected isfrom one or more of the medical device itself or input from a userassociated with the medical device, wherein the input is received at themedical device or a distinct computing device; transmit the collecteddata to a remote computing device; and receive, from the remotecomputing device, at the medical device or a computing device associatedwith the medical device, at least one of: (a) an adjustment to anoperation of the medical device, (b) a suggested adjustment to anoperation of the medical device, or (c) a notification about the medicaldevice or the patient associated with the medical device, wherein theremote computing device determines the adjustment, suggested adjustment,or notification using an artificial intelligence (AI) engine orhard-coded rules based on crowd-sourced data obtained from at leastother distinct patients or distinct medical devices.
 22. The one or morehardware-based non-transitory computer-readable memory devices of claim21, wherein the AI engine of the remote computing device trains a modelof data and deploys a predictive model to determine the adjustment,suggested adjustment, or notification for the medical device.
 23. Theone or more hardware-based non-transitory computer-readable memorydevices of claim 21, wherein the suggested adjustment is presented tothe user in the form of a notification on the medical device's displayor the computing device's display that is associated with the medicaldevice or patient.
 24. The one or more hardware-based non-transitorycomputer-readable memory devices of claim 23, wherein the suggestedadjustment or the notification pertain to a hardware component for themedical device, a dosage administered by the medical device, or anoperational parameter of the medical device.
 25. The one or morehardware-based non-transitory computer-readable memory devices of claim21, wherein the adjustment to the medical device's operation isautomatically administered by the medical device upon receipt.
 26. Theone or more hardware-based non-transitory computer-readable memorydevices of claim 21, wherein the executed instructions further cause themedical device or the computing device associated with the medicaldevice to verify that the received adjustment, suggested adjustment, ornotification are in-line with criteria for the patient, wherein thecriteria are unique to the patient's treatment plan.
 27. The one or morehardware-based non-transitory computer-readable memory devices of claim26, wherein the criteria include limits or configurations based on thepatient's treatment plan.
 28. The one or more hardware-basednon-transitory computer-readable memory devices of claim 21, wherein theadjustment, suggested adjustment, or notification is derived fromcrowd-sourced information for medical device treatments associated withusers that are relatable to the patient.
 29. The one or morehardware-based non-transitory computer-readable memory devices of claim28, wherein relatable users are programmatically identified using thehard-coded rules or rules derived from the AI engine.
 30. The one ormore hardware-based non-transitory computer-readable memory devices ofclaim 21, wherein the user that inputs the data is either a patientbeing treated by the medical device or a caregiver.
 31. A remote serviceconfigured to generate and transmit treatment adjustments, suggestedtreatment adjustments, or notifications to patients undergoing treatmenton a medical device, comprising: one or more processors; ahardware-based memory device having computer-executable instructionswhich, when executed by the one or more processors, causes the remoteservice to: instantiate an artificial intelligence (AI) engine; receivedata associated with one or both of a plurality of patients or aplurality of medical devices that are associated with patients; process,using the AI engine, the received data, in which the processing includesidentifying patterns within the data, wherein the identified patternsare based on multiple treatment sessions derived from the plurality ofpatients or the plurality of medical devices, and the patterns pertainto at least an operational configuration of the plurality of medicaldevices based on set treatment parameters, in which the identifiedpatterns are utilized to improve patient treatment; and determine, usingthe AI engine's identified patterns, unique treatments for patients ormedical devices, the predictions being tailored to an adjustment of amedical device, a suggested adjustment to the medical device, or anotification about the medical device or a patient.
 32. The remoteservice of claim 31, wherein the executed instructions further cause theremote service to determine the adjustment to an operation of a medicaldevice associated with a unique user.
 33. The remote service of claim31, wherein the executed instructions further cause the remote serviceto determine the suggested adjustment to an operation of a medicaldevice associated with a unique user.
 34. The remote service of claim31, wherein the executed instructions further cause the remote serviceto: determine an item of information about a unique medical device or aunique patient that is associated with patients; transmit the determineditem of information in the notification to the unique medical device orthe medical device or computing device associated with the uniquepatient.
 35. The remote service of claim 31, wherein the suggestedadjustment or the notification pertain to a hardware component for themedical device, a dosage administered by the medical device, or anoperational parameter of the medical device.
 36. The remote service ofclaim 31, wherein the patterns are identified at least partially basedon commonalities among one or both of the plurality of patients or theplurality of medical devices.
 37. A method performed by a computingdevice configured to generate and transmit treatment adjustments,suggested treatment adjustments, or notifications to patients undergoingtreatment on a medical device, comprising: instantiating, using one ormore processors, an artificial intelligence (AI) engine; receiving,using the one or more processors, data associated with one or both of aplurality of patients or a plurality of medical devices associated withpatients; processing, using the one or more processors and the AIengine, the received data, in which the processing includes identifyingpatterns within the data, wherein the identified patterns are based onmultiple treatment sessions derived from the plurality of patients orthe plurality of medical devices, and the patterns pertain to at leastan operational configuration of the plurality medical devices based onset treatment parameters, in which the identified patterns are utilizedto modify patient treatment; and determining, using the one or moreprocessors and the AI engine's identified patterns, unique treatmentsfor patients or medical devices, the predictions being tailored to anadjustment of a medical device, a suggested adjustment to the medicaldevice, or a notification about the medical device or a patient.
 38. Themethod of claim 37, wherein the suggested adjustment or the notificationpertain to a hardware component for the medical device, a dosageadministered by the medical device, or an operational parameter of themedical device.
 39. The method of claim 37, wherein the patterns areidentified at least partially based on commonalities among one or bothof the plurality of patients or the plurality of medical devices. 40.The method of claim 37, further comprising: determining, using the oneor more processors, an item of information about a unique medical deviceor a unique patient that is associated with a medical device or acomputing device; and transmitting, using the one or more processors,the determined item of information in the notification to the uniquemedical device or the medical device or computing device associated withthe unique patient.